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Record W2055707038 · doi:10.1002/fam.928

Fire losses in selected property classifications of non‐residential, commercial and residential wood buildings. Part 1: hotels/motels and care homes for aged

2006· article· en· W2055707038 on OpenAlexafffundabout
Leslie R. Richardson

Bibliographic record

VenueFire and Materials · 2006
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsIntertek (Canada)
FundersFPInnovations
KeywordsFire safetySmokeForensic engineeringArchitectural engineeringFire protectionFlammabilityBuilding codeEngineeringEnvironmental scienceCivil engineeringTransport engineeringWaste management

Abstract

fetched live from OpenAlex

Abstract In an attempt to evaluate the adequacy of building code requirements for selected classifications of non‐residential, commercial and residential wood buildings, researchers at Forintek Canada Corp. have examined Canadian and American fire loss statistics and compared fire losses for the selected classifications of wood buildings with those for similar buildings of non‐combustible construction. They have also examined causal factors associated with fires in those structures, extent of flame and smoke spread, ability of sprinkler systems and building construction to minimize fire losses, and outcomes of fire events. Because of the volume of information that was analysed, the results are being reported through three separate papers. This, the first, presents the ‘big picture’ with respect to fire losses in the selected classifications of non‐residential, commercial and residential structures, and discusses in detail fire losses for hotel/motel properties and care homes for the aged. Copyright © 2006 John Wiley & Sons, Ltd.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.298
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2006
Admission routes3
Has abstractyes

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